Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 340690 Machine Vision 1 3.0 3 ★ Lin, Huei-Yung 4CSIE3 24 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	340690	Machine Vision	1	3.0	3	★	Lin, Huei-Yung	4CSIE3	24	0

Course Plan：

Instructor Lin, Huei-Yung　Office Hours

Email lin@ntut.edu.tw

Last Updated 2024-12-16 23:44:37

Course Objective 本課程介紹機器視覺之基本概念、原理及衍生應用，同時輔助以開放的影像函式庫來實作訓練；課程中由基礎的基本影像分析入門，並逐步導入機器視覺理論與方法，二維影像校正與立體視覺等內容；課程中將介紹機器視覺系統的相關資源與實作能力的培養，並進一步延伸至影像量測、機器學習與機器人領域的範例應用。 The field of machine vision has been growing at a fast pace. As in most developing fields, not all aspects of machine vision that are of interest to active researchers are useful to the designers and users of a vision system for a specific application. This course is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithms in practical applications are provided, and engineering aspects of techniques are emphasized.

Course Schedule Introduction: Machine Vision, Image Geometry, Sampling and Quantization, Image Definitions, Levels of Computation (Week 1) Binary Image Processing: Thresholding, Geometric Properties, Projections, Run-Length Encoding, Binary Algorithms, Morphological Operators, OCR (Weeks 2, 3) Regions: Regions and Edges, Region Segmentation, Region Segmentation, Split and Merge, Region Growing (Weeks 4,5,6) Image Filtering: Histogram Modification, Linear Systems, Linear Filters, Median Filter, Gaussian Smoothing (Weeks 7, 8, 9) Edge Detection: Gradient, Steps in Edge Detection, Second Derivative Operators, Laplacian of Gaussian (Weeks 10, 11) Contours: Geometry of Curves, Digital Curves, Curve Fitting, Polynomial Representation, Circular Arcs, Conic Sections, Spline Curves (Weeks 12, 13) Texture: Statistical Methods of Texture Analysis, Structural Analysis of Ordered Texture, Model-Based Methods for Texture Analysis, Shape from Texture (Weeks 14, 15) Color: Color Physics, Terminology, Perception, Processing, Constancy (Week 16) Curves and Surfaces: Geometry of Curves and Surfaces, Curve and Surface Representation, Surface Interpolation, Approximation, Segmentation (Weeks 17, 18)

Evaluation and grading policy Homework (50 %) Midterm and Final Exams (50 %)

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Machine Vision, by Jain, Kasturi, and Schunck, McGraw-Hill

The access to curricular consultation Office Hours

The course corresponds to the SDGs SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）

Note
可透過 e-mail 及課程系統聯繫

Instructor	Lin, Huei-Yung　Office Hours
Email	lin@ntut.edu.tw
Last Updated	2024-12-16 23:44:37
Course Objective	本課程介紹機器視覺之基本概念、原理及衍生應用，同時輔助以開放的影像函式庫來實作訓練；課程中由基礎的基本影像分析入門，並逐步導入機器視覺理論與方法，二維影像校正與立體視覺等內容；課程中將介紹機器視覺系統的相關資源與實作能力的培養，並進一步延伸至影像量測、機器學習與機器人領域的範例應用。 The field of machine vision has been growing at a fast pace. As in most developing fields, not all aspects of machine vision that are of interest to active researchers are useful to the designers and users of a vision system for a specific application. This course is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithms in practical applications are provided, and engineering aspects of techniques are emphasized.
Course Schedule	Introduction: Machine Vision, Image Geometry, Sampling and Quantization, Image Definitions, Levels of Computation (Week 1) Binary Image Processing: Thresholding, Geometric Properties, Projections, Run-Length Encoding, Binary Algorithms, Morphological Operators, OCR (Weeks 2, 3) Regions: Regions and Edges, Region Segmentation, Region Segmentation, Split and Merge, Region Growing (Weeks 4,5,6) Image Filtering: Histogram Modification, Linear Systems, Linear Filters, Median Filter, Gaussian Smoothing (Weeks 7, 8, 9) Edge Detection: Gradient, Steps in Edge Detection, Second Derivative Operators, Laplacian of Gaussian (Weeks 10, 11) Contours: Geometry of Curves, Digital Curves, Curve Fitting, Polynomial Representation, Circular Arcs, Conic Sections, Spline Curves (Weeks 12, 13) Texture: Statistical Methods of Texture Analysis, Structural Analysis of Ordered Texture, Model-Based Methods for Texture Analysis, Shape from Texture (Weeks 14, 15) Color: Color Physics, Terminology, Perception, Processing, Constancy (Week 16) Curves and Surfaces: Geometry of Curves and Surfaces, Curve and Surface Representation, Surface Interpolation, Approximation, Segmentation (Weeks 17, 18)
Evaluation and grading policy	Homework (50 %) Midterm and Final Exams (50 %)
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Machine Vision, by Jain, Kasturi, and Schunck, McGraw-Hill
The access to curricular consultation	Office Hours
The course corresponds to the SDGs	SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
Note	可透過 e-mail 及課程系統聯繫